: a convolutional neural network based architecture for text classification.

This paper presents, , a novel Convolutional Neural Network (CNN) based architecture for binary and multi-class text classification problems. Most of the existing CNN-based models use one-dimensional convolving filters, where each filter specializes in extracting features of a particular input word embeddings (Sentence Matrix). These features can be termed as intra-sentence features.

Predictive modeling for suspicious content identification on Twitter.

The wide popularity of Twitter as a medium of exchanging activities, entertainment, and information is attracted spammers to discover it as a stage to spam clients and spread misinformation. It poses the challenge to the researchers to identify malicious content and user profiles over Twitter such that timely action can be taken. Many previous works have used different strategies to overcome this challenge and combat spammer activities on Twitter.

Towards a super-resolution based approach for improved face recognition in low resolution environment.

The video surveillance activity generates a vast amount of data, which can be processed to detect miscreants. The task of identifying and recognizing an object in surveillance data is intriguing yet difficult due to the low resolution of captured images or video. The super-resolution approach aims to enhance the resolution of an image to generate a desirable high-resolution one.